A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
This work addresses diabetic retinopathy diagnosis, an important medical domain, but is incremental as it builds on existing methods like Gaussian scale space and support vector machines.
The paper tackles the problem of detecting and classifying exudates in diabetic retinopathy images to predict disease severity, achieving a sensitivity of 96.54% and prediction of 98.35% on the DIARETDB1V2 database.
In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.